A qualitative study to explore views of patients’, carers’ and mental health professionals’ to inform cultural adaptation of CBT for psychosis (CBTp) in China
Bibliographic record
Abstract
BACKGROUND: The evidence for effectiveness of Cognitive Behaviour Therapy (CBT) is robust and the national organizations in the United Kingdom and the United States recommend its use. It is not utilized to its full potential in low and middle-income countries. Adaptation of CBT treatment to the target culture may facilitate its uptake. This study explored views of patients with schizophrenia, their caregivers, and mental health professionals for the purpose of cultural adaptation of CBT. METHOD: The project was conducted in a teaching hospital in China. Systematic content and question analysis were the techniques we used to analyse the data generated in a series of qualitative interviews (N 45) in China. After identification of emerging themes and categories we compared and contrasted the themes across different interviews recursively. Triangulation of themes and concepts was undertaken to compare further and contrast the data from the different participating groups. RESULTS: This work highlighted the barriers in therapy as well as opportunities for use of CBT in that environment. Patients and their carers in China use a bio-psycho-spiritual-social model of illness. CBT is not commonly used to help those with schizophrenia in China. CONCLUSIONS: This study will facilitate the therapists using CBT for people with psychosis in China. These results require to be tested in clinical trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".